
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import PoolFormerImageProcessor




class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def __init__(   self						:Optional[Any]	,  a						:Union[str, Any]	,  a						:Dict=7	,  a						:List[Any]=3	,  a						:Dict=3_0	,  a						:Tuple=4_0_0	,  a						:Optional[int]=True	,  a						:Dict=None	,  a						:str=0.9	,  a						:int=None	,  a						:Tuple=True	,  a						:int=[0.5, 0.5, 0.5]	,  a						:Tuple=[0.5, 0.5, 0.5]	,  )						->					Union[str, Any]:
								__UpperCamelCase      :  int       =   size if size is not None else {"shortest_edge": 3_0}
								__UpperCamelCase      :  Tuple       =   crop_size if crop_size is not None else {"height": 3_0, "width": 3_0}
								__UpperCamelCase      :  Optional[Any]       =   parent
								__UpperCamelCase      :  Union[str, Any]       =   batch_size
								__UpperCamelCase      :  Optional[int]       =   num_channels
								__UpperCamelCase      :  Tuple       =   min_resolution
								__UpperCamelCase      :  str       =   max_resolution
								__UpperCamelCase      :  Optional[int]       =   do_resize_and_center_crop
								__UpperCamelCase      :  Optional[int]       =   size
								__UpperCamelCase      :  Dict       =   crop_pct
								__UpperCamelCase      :  Optional[Any]       =   crop_size
								__UpperCamelCase      :  Tuple       =   do_normalize
								__UpperCamelCase      :  List[str]       =   image_mean
								__UpperCamelCase      :  int       =   image_std







	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[str]:
								return {
								    "size": self.size,
								    "do_resize_and_center_crop": self.do_resize_and_center_crop,
								    "crop_pct": self.crop_pct,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}




@require_torch
@require_vision
class 					lowerCamelCase__				(							lowercase__	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	PoolFormerImageProcessor if is_vision_available() else None







	def   _lowerCamelCase							(   self						:Tuple		)						->					Union[str, Any]:
								__UpperCamelCase      :  str       =   PoolFormerImageProcessingTester(self		)







	@property
	def   _lowerCamelCase							(   self						:Optional[int]		)						->					Dict:
								return self.image_processor_tester.prepare_image_processor_dict()







	def   _lowerCamelCase							(   self						:Any		)						->					List[str]:
								__UpperCamelCase      :  Optional[int]       =   self.image_processing_class(**self.image_processor_dict		)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,  "do_resize_and_center_crop"		)		)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,  "size"		)		)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,  "crop_pct"		)		)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,  "do_normalize"		)		)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,  "image_mean"		)		)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,  "image_std"		)		)







	def   _lowerCamelCase							(   self						:int		)						->					List[Any]:
								__UpperCamelCase      :  List[Any]       =   self.image_processing_class.from_dict(self.image_processor_dict		)
								self.assertEqual(image_processor.size	,  {"shortest_edge": 3_0}		)
								self.assertEqual(image_processor.crop_size	,  {"height": 3_0, "width": 3_0}		)

								__UpperCamelCase      :  str       =   self.image_processing_class.from_dict(self.image_processor_dict	,  size=4_2	,  crop_size=8_4		)
								self.assertEqual(image_processor.size	,  {"shortest_edge": 4_2}		)
								self.assertEqual(image_processor.crop_size	,  {"height": 8_4, "width": 8_4}		)







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					int:
								pass







	def   _lowerCamelCase							(   self						:str		)						->					int:
								__UpperCamelCase      :  Any       =   self.image_processing_class(**self.image_processor_dict		)
								# create random PIL images
								__UpperCamelCase      :  Any       =   prepare_image_inputs(self.image_processor_tester	,  equal_resolution=SCREAMING_SNAKE_CASE_		)
								for image in image_inputs:
															self.assertIsInstance(SCREAMING_SNAKE_CASE_	,  Image.Image		)

								# Test not batched input
								__UpperCamelCase      :  Union[str, Any]       =   image_processing(image_inputs[0]	,  return_tensors="pt"		).pixel_values

								self.assertEqual(
								    encoded_images.shape	,  (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )	,  )

								# Test batched
								__UpperCamelCase      :  Dict       =   image_processing(SCREAMING_SNAKE_CASE_	,  return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape	,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )	,  )







	def   _lowerCamelCase							(   self						:int		)						->					Dict:
								__UpperCamelCase      :  List[Any]       =   self.image_processing_class(**self.image_processor_dict		)
								# create random numpy tensors
								__UpperCamelCase      :  Tuple       =   prepare_image_inputs(self.image_processor_tester	,  equal_resolution=SCREAMING_SNAKE_CASE_	,  numpify=SCREAMING_SNAKE_CASE_		)
								for image in image_inputs:
															self.assertIsInstance(SCREAMING_SNAKE_CASE_	,  np.ndarray		)

								# Test not batched input
								__UpperCamelCase      :  str       =   image_processing(image_inputs[0]	,  return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape	,  (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )	,  )

								# Test batched
								__UpperCamelCase      :  Any       =   image_processing(SCREAMING_SNAKE_CASE_	,  return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape	,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )	,  )







	def   _lowerCamelCase							(   self						:List[Any]		)						->					Union[str, Any]:
								__UpperCamelCase      :  int       =   self.image_processing_class(**self.image_processor_dict		)
								# create random PyTorch tensors
								__UpperCamelCase      :  str       =   prepare_image_inputs(self.image_processor_tester	,  equal_resolution=SCREAMING_SNAKE_CASE_	,  torchify=SCREAMING_SNAKE_CASE_		)
								for image in image_inputs:
															self.assertIsInstance(SCREAMING_SNAKE_CASE_	,  torch.Tensor		)

								# Test not batched input
								__UpperCamelCase      :  Optional[int]       =   image_processing(image_inputs[0]	,  return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape	,  (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )	,  )

								# Test batched
								__UpperCamelCase      :  Optional[Any]       =   image_processing(SCREAMING_SNAKE_CASE_	,  return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape	,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )	,  )

"""simple docstring"""






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
					import torch



@require_torch
@require_optimum
@slow
class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  tokenizer('''This is me'''      ,return_tensors='''pt'''			)

				lowerCAmelCase												=  model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				lowerCAmelCase												=  model.generate(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

					self.assertFalse(
					    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

					lowerCAmelCase												=  model_reloaded.generate(**SCREAMING_SNAKE_CASE_			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)






			def 					UpperCAmelCase							(self  :	str			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
						model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  model.reverse_bettertransformer()
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)


